We present a new approach to extract the license from an image sequence of moving vehicles. The approach includes the following components: 1) license plate localization; 2) feature extraction and tracking; 3) perspective distortion correction; 4) binarization. We model the binarization of characters as a Markov random field (MRF), where the randomness is used to describe the uncertainty in pixel label assignment. With the MRF modeling, the extraction of characters is formulated as the problem of maximizing the a posteriori probability based on given prior and observations. A genetic algorithm with local greedy mutation operator is employed to optimize the objective function based on MRF modeling. In the experiments, we compared our results with other two methods that were evaluated. Our method has demonstrated better performance